LAUSR

Studies in the last years have proved the outstanding performance of deep learning for computer vision tasks in the remote sensing field, such as disparity estimation. However, available datasets mostly focus on close-range applications like autonomous driving or robot manipulation. To reduce the domain gap while training we present SyntCities, a synthetic dataset resembling the aerial imagery on urban areas. The pipeline used to render the images is based on 3-D modeling, which helps to avoid acquisition costs, provides subpixel accurate dense ground truth and simulates different illumination conditions. The dataset additionally provides multiclass semantic maps and can be converted to point cloud format to benefit a wider research community. We focus on the task of disparity estimation and evaluate the performance of the traditional semiglobal matching and state-of-the-art architectures, trained with SyntCities and other datasets, on real aerial and satellite images. A comparison with the widely used SceneFlow dataset is also presented. Strategies using a mixture of both real and synthetic samples are studied as well. Results show significant improvements in terms of accuracy for the disparity maps.